Generalized Feature Embedding for Supervised, Unsupervised, and Online Learning Tasks
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Feature embedding is an emerging research area which intends to transform features from the original space into a new space to support effective learning. Many feature embedding algorithms exist, but they often suffer from several major drawbacks, including (1) only handle single feature types, or users have to clearly separate features into different feature views and supply such information for feature embedding learning; (2) designed for either supervised or unsupervised learning tasks, but not for both; and (3) feature embedding for new out-of-training samples have to be obtained through a retraining phase, therefore unsuitable for online learning tasks. In this paper, we propose a generalized feature embedding algorithm, GEL, for both supervised, unsupervised, and online learning tasks. GEL learns feature embedding from any type of data or data with mixed feature types. For supervised learning tasks with class label information, GEL leverages a Class Partitioned Instance Representation (CPIR) process to arrange instances, based on their labels, as a dense binary representation via row and feature vectors for feature embedding learning. If class labels are unavailable, CPIR is naturally degenerated and treats all instances as one class. Based on the CPIR representation, GEL uses eigenvector decomposition to convert the proximity matrix into a low-dimensional space. For new out-of-training samples, their low-dimensional representation are derived through a direct conversion without a retraining phase. The learned numerical embedding features can be directly used to represent instances for effective learning. Experiments and comparisons on 28 datasets, including categorical, numerical, and ordinal features, demonstrate that embedding features learned from GEL can effectively represent the original instances for clustering, classification, and online learning.
KeywordsRepresentation learning Feature embedding Dimension reduction Supervised learning Clustering Online learning
- Abdi, H., & Valentin, D. (2007). Multiple correspondence analysis. In Encyclopedia of measurement and statistics (pp. 651–657).Google Scholar
- Alamuri, M., Surampudi, B.R., Negi, A. (2014). A survey of distance/similarity measures for categorical data. In 2014 International joint conference on neural networks (IJCNN) (pp. 1907–1914).Google Scholar
- Aljarah, I. (2016). https://www.kaggle.com/aljarah/xapi-edu-data.
- Argyriou, A., & Evgeniou, T. (2007). Multi-task feature learning. In Proceedings of neural information processing systems (NIPS).Google Scholar
- Babenko, A., & Lempitsky, V. (2015). Aggregating local deep features for image retrieval. In Proceedings of the IEEE international conference on computer vision (pp. 1269–1277).Google Scholar
- Benoit, K., & Nulty, P. (2016). quanteda: quantitative analysis of textual data. R package version 0.9, 8.Google Scholar
- Choi, S.-S., Cha, S.-H., Tappert, C.C. (2010). A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics, 8(1), 43–48.Google Scholar
- de Leeuw, J. (2011). Principal component analysis of binary data. applications to roll-call-analysis. Department of statistics, UCLA.Google Scholar
- Gal, Y., Chen, Y., Ghahramani, Z. (2015). Latent gaussian processes for distribution estimation of multivariate categorical data. In Proceedings of the 32nd international conference on machine learning (ICML-15) (pp. 645–654).Google Scholar
- Golinko, E., & Zhu, X. (2017). Gfel: Generalized feature embedding learning using weighted instance matching. In 2017 IEEE International conference on information reuse and integration (IRI) (pp. 235–244).Google Scholar
- Greenacre, M. (2007). Correspondence analysis in practice. CRC press.Google Scholar
- Greene, D. (2016). http://mlg.ucd.ie/datasets/bbc.html.
- Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research 3:1157–1182.Google Scholar
- Hsu, C.-W., Chang, C.-C., Lin, C.-J., et al. (2003). A practical guide to support vector classification.Google Scholar
- Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of ACM multimedia conference.Google Scholar
- Juan, A., & Vidal, E. (2004). Bernoulli mixture models for binary images. In Proceedings of the 17th international conference on Pattern recognition, 2004. ICPR 2004, (Vol. 3 pp. 367–370). IEEE.Google Scholar
- Kaban, A., Bingham, E., Hirsimäki, T. (2004). Learning to read between the lines The aspect bernoulli model. In Proceedings of the 2004 SIAM international conference on data mining (pp. 462–466). SIAM.Google Scholar
- Kaggle. (2017). https://www.kaggle.com.
- Krijthe, J. (2015). Rtsne: T-distributed stochastic neighbor embedding using barnes-hut implementation. R package version 0.10, http://CRAN.R-project.org/package=Rtsne.
- Lee, S. (2009). Principal components analysis for binary data. PhD thesis: Texas A&M University.Google Scholar
- Lichman, M. (2013). UCI machine learning repository.Google Scholar
- van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(Nov), 2579–2605.Google Scholar
- Meyer, D., & Buchta, C. proxy: Distance and Similarity Measures, 2016. R package version 0.4-16.Google Scholar
- Muhlbaier, M.D., & Polikar, R. (2007). An ensemble approach for incremental learning in nonstationary environments. In International workshop on multiple classifier systems (pp. 490–500). Berlin: Springer.Google Scholar
- Müller, B., Reinhardt, J., Strickland, M.T. (2012). Neural networks: an introduction. Berlin: Springer Science & Business Media.Google Scholar
- Najafi, A., Motahari, A., Rabiee, H.R. (2017). Reliable learning of bernoulli mixture models. arXiv:1710.02101.
- Nenadic, O., & Greenacre, M. (2007). Correspondence analysis in r, with two-and three-dimensional graphics The ca package. Journal of Statistical Software.Google Scholar
- Pan, S., Wu, J.W., Zhu, X., Zhang, C., Wang, Y. (2016). Tri-party deep network representation. In Proc. of international joint conference on artificial intelligence.Google Scholar
- Ramos, J. (2003). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning.Google Scholar
- Rokach, L., & Maimon, O. (2005). Decision trees. Data mining and knowledge discovery handbook, pp. 165–192.Google Scholar
- Romero, C., Ventura, S., Espejo, P.G., Hervás, C. (2008). Data mining algorithms to classify students. In Educational data mining 2008.Google Scholar
- Shlens, J. (2014). A tutorial on principal component analysis. arXiv:1404.1100.
- Shmelkov, K., Schmid, C., Alahari, K. (2017). Incremental learning of object detectors without catastrophic forgetting. arXiv:1708.06977.
- Strange, H., & Zwiggelaar, R. (2011). A generalised solution to the out-of-sample extension problem in manifold learning. In AAAI (pp. 293–296).Google Scholar
- Tsymbal, A., Puuronen, S., Pechenizkiy, M., Baumgarten, M., Patterson, D.W. (2002). Eigenvector-based feature extraction for classification. In FLAIRS Conference (pp. 354–358).Google Scholar
- Xie, J., Szymanski, B.K., Zaki, M.J. (2010). Learning dissimilarities for categorical symbols. FSDM, 10, 97–106.Google Scholar
- Zhang, D., Yin, J., Zhu, X., Zhang, C. (2017). User profile preserving social network embedding. In Proc. of international joint conference on artificial intelligence.Google Scholar
- Zhang, H. (2004). The optimality of naive bayes. AA, 1(2), 3.Google Scholar
- Zhang, L., Zhang, Q., Zhang, L., Tao, D., Huang, X., Du, B. (2015). Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recognition, 48(10).Google Scholar
- Zhang, P., Zhu, X., Shi, Y. (2008). Categorizing and mining concept drifting data streams. In ACM SIGKDD Conference (pp. 812–820).Google Scholar